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Key benefits

Load, manipulate and analyze data from different sources

Gain a deeper understanding of fundamentals of applied statistics

A practical guide to performing data analysis in practice

Description

Frequently the tool of choice for academics, R has spread deep into the private sector and can be found in the production pipelines at some of the most advanced and successful enterprises. The power and domain-specificity of R allows the user to express complex analytics easily, quickly, and succinctly. With over 7,000 user contributed packages, it’s easy to find support for the latest and greatest algorithms and techniques. Starting with the basics of R and statistical reasoning, Data Analysis with R dives into advanced predictive analytics, showing how to apply those techniques to real-world data though with real-world examples. Packed with engaging problems and exercises, this book begins with a review of R and its syntax. From there, get to grips with the fundamentals of applied statistics and build on this knowledge to perform sophisticated and powerful analytics. Solve the difficulties relating to performing data analysis in practice and find solutions to working with “messy data”, large data, communicating results, and facilitating reproducibility. This book is engineered to be an invaluable resource through many stages of anyone’s career as a data analyst.

Who is this book for?

Whether you are learning data analysis for the first time, or you want to deepen the understanding you already have, this book will prove to an invaluable resource. If you are looking for a book to bring you all the way through the fundamentals to the application of advanced and effective analytics methodologies, and have some prior programming experience and a mathematical background, then this is for you.

What you will learn

Navigate the R environment

Describe and visualize the behavior of data and relationships between data

Gain a thorough understanding of statistical reasoning and sampling

Employ hypothesis tests to draw inferences from your data

Learn Bayesian methods for estimating parameters

Perform regression to predict continuous variables

Apply powerful classification methods to predict categorical data

Handle missing data gracefully using multiple imputation

Identify and manage problematic data points

Employ parallelization and Rcpp to scale your analyses to larger data

Put best practices into effect to make your job easier and facilitate reproducibility

What do you get with Print?

Instant access to your digital copy whilst your Print order is Shipped

Paperback book shipped to your preferred address

Redeem a companion digital copy on all Print orders

Access this title in our online reader with advanced features

DRM FREE - Read whenever, wherever and however you want

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Amazon verified reviews

Abhijit Dasgupta May 05, 2016

I think that this is a wonderful introduction, not just to R programming, but to statistics as well. The author does well to provide a pretty solid conceptual introduction to all the topics he introduces, as well as programming examples to demonstrate the concepts. The style is quite conversational and easy to read. The chosen topics are very appropriate for a modern data analyst, encompassing classical statistics with some machine learning and predictive analytics, as well as practical issues of data input, parallelization and reproducibility. This isn't a staid old classical text!!!I enjoyed reading this book a lot, both for it's style and its comprehensiveness. I could certainly see using this book in introductory data science workshops as a reference.One thing I would have liked is a section on further reading for each chapter. The chapters serve as good introductions to a wide range of topics, and it would have been good to provide pointers to more in depth reading, specially some of the material on Bayesian statistics, predictive analytics, Rcpp, data inputs and reproducibility, which are often not addressed in one place. Each of these topics have good R-based books available.

Amazon Verified review

Sandra C. Apr 04, 2018

I don't normally leave reviews, but the author does a great job of drawing the reader in and most importantly keeping your interest! Comprehensive, fun, and plenty of very easy to follow examples. I would highly recommend it to anyone!

Amir Aghamousa Mar 18, 2016

I would congratulate to Tony Fischetti for his book. This book explains nicely Data Analysis with the help of R from basic to more advanced topics without needing prior knowledge for readers in statistics or R. It starts from introducing R and basic statistics and their applications in Data Analysis. It continues with elaborating important topics like Hypothesis Testing, Bayesian Statistics, MCMC, Regression, K-NN algorithm, Random Forest, ... which are used frequently in Data Analysis in science and industry. All topics are followed with practical hints and examples in R. The final chapters complete the subject by dealing with essential topics like Accessing to Databases, Handling Messy Data and Big Data.To sum up, this is a great book in Data Analysis and would be recommended to students or researchers from any fields in science or industry who wish to deal with data. The book is also suitable for teaching in colleges or universities as a course of Data Analysis or Applied Statistics.

Dimitri Shvorob Mar 03, 2016

Packt's conveyor is not slowing down: only three months ago I surveyed their fresh crop of "data science with R" offerings"Mastering Predictive Analytics with R" by Forte, £32.99"Mastering Machine Learning with R" by Lesmeister, £34.99"R Data Analysis Cookbook" by Viswanathan and Viswanathan, £29.99"Machine Learning with R Cookbook" by Yu-Wei, £30.99and now there are four more:"Unsupervised Learning with R" by Pacheco, £25.99"Data Analysis with R" by Fischetti, £34.99"Learning Predictive Analytics with R" by Mayor, £31.99"Mastering Data Analysis with R" by Daroczi, £34.99So far I have gone through the first two "new" titles, and had a peek at the other two. Pacheco's book is a clear "pass", Mayor's and Daroczi's look promising [UPD: I was wrong] - and finally, Fischetti's is ... a pleasant surprise, a rare careful, original book in Pack't sea of low-quality, low-value-added quickies. It is so nice to see an author (a) thinking about what he wants to present and (b) crafting his text - as opposed to walking through an oh-so-familiar checklist of machine-learning algorithms and regaling readers with half-competent, shallow digests of "theory" found in better books. Now, since this book, surprisingly, covers "proper" statistics, not the machine-learning algorithms, you may need a book on those - I would recommend "Introduction to Statistical Learning" by Witten et al. - but "Data Analysis with R" definitely gets my endorsement, as a friendly yet substantial introduction.UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.

Luis Jose Muãiz Rascado Mar 18, 2016

Amazing book

Data Analysis with R: Click here to enter text.

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